Development strategy of non-GMO organism for increased hemoproteins in Corynebacterium glutamicum: a growth-acceleration-targeted evolution.

Heme, found in hemoproteins, is a valuable source of iron, an essential mineral. The need for an alternative hemoprotein source has emerged due to the inherent risks of large-scale livestock farming and animal proteins. Corynebacterium glutamicum, regarded for Qualified Presumption of Safety or Generally Recognized as Safe, can biosynthesize hemoproteins. C. glutamicum single-cell protein (SCP) can be a valuable alternative hemoprotein for supplying heme iron without adversely affecting blood fat levels. We constructed the chemostat culture system to increase hemoprotein content in C. glutamicum SCP. Through adaptive evolution, hemoprotein levels could be naturally increased to address oxidative stress resulting from enhanced growth rate. In addition, we used several specific plasmids containing growth-accelerating genes and the hemA promoter to expedite the evolutionary process. Following chemostat culture for 15 days, the plasmid in selected descendants was cured. The evolved strains showed improved specific growth rates from 0.59 h-1 to 0.62 h-1, 20% enhanced resistance to oxidative stress, and increased heme concentration from 12.95 µg/g-DCW to 14.22-15.24 µg/g-DCW. Notably, the putative peptidyl-tRNA hydrolase-based evolved strain manifested the most significant increase (30%) of hemoproteins. This is the first report presenting the potential of a growth-acceleration-targeted evolution (GATE) strategy for developing non-GMO industrial strains with increased bio-product productivity.

Calibration-free blood pressure estimation based on a convolutional neural network.

In this study, we conducted research on a deep learning-based blood pressure (BP) estimation model suitable for wearable environments. To measure BP while wearing a wearable watch, it needs to be considered that computing power for signal processing is limited and the input signals are subject to noise interference. Therefore, we employed a convolutional neural network (CNN) as the BP estimation model and utilized time-series electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which are quantifiable in a wearable context. We generated periodic input signals and used differential and thresholding methods to decrease noise in the preprocessing step. We then applied a max-pooling technique with filter sizes of 2 × 1 and 5 × 1 within a 3-layer convolutional neural network to estimate BP. Our method was trained, validated, and tested using 2.4 million data samples from 49 patients in the intensive care unit. These samples, totaling 3.1 GB were obtained from the publicly accessible MIMIC database. As a result of a test with 480,000 data samples, the average root mean square error in BP estimation was 3.41, 5.80, and 2.78 mm Hg in the prediction of pulse pressure, systolic BP (SBP), and diastolic BP (DBP), respectively. The cumulative error percentage less than 5 mm Hg was 68% and 93% for SBP and DBP, respectively. In addition, the cumulative error percentage less than 15 mm Hg was 98% and 99% for SBP and DBP. Subsequently, we evaluated the impact of changes in input signal length (1 cycle vs. 30 s) and the introduction of noise on BP estimation results. The experimental results revealed that the length of the input signal did not significantly affect the performance of CNN-based analysis. When estimating BP using noise-added ECG signals, the mean absolute error (MAE) for SBP and DBP was 9.72 and 6.67 mm Hg, respectively. Meanwhile, when using noise-added PPG signals, the MAE for SBP and DBP was 26.85 and 14.00 mm Hg, respectively. Therefore, this study confirmed that using ECG signals rather than PPG signals is advantageous for noise reduction in a wearable environment. Besides, short sampling frames without calibration can be effective as input signals. Furthermore, it demonstrated that using a model suitable for information extraction rather than a specialized deep learning model for sequential data can yield satisfactory results in BP estimation.

Macromonas nakdongensis sp. nov., Isolated from Freshwater and Characterization of Bacteriophage BK-30P-The First Phage That Infects Genus Macromonas.

A Gram-stain-negative, non-motile, non-pigmented, rod-shaped bacterium was isolated from a freshwater sample of Nakdong River in South Korea and designated as strain BK-30T. An analysis of the 16S rRNA gene sequence of strain BK-30T revealed its closest phylogenetic neighbors were members of the genus Macromonas. Specifically, the strain formed a robust clade with Macromonas bipunctata DSM 12705T, sharing 98.4% similarity in their 16S rRNA gene sequences. The average nucleotide identity value between strain BK-30T and M. bipunctata DSM 12705T was 79.8%, and the genome-to-genome distance averaged 21.3%, indicating the representation of a novel genomic species. Strain BK-30T exhibited optimum growth at 30 °C and pH 7.0, in the absence of NaCl. The major respiratory isoprenoid quinone identified was ubiquinone-8 (Q-8). The principal fatty acids detected were C16:1ω7c and/or C16:1ω6c (49.6%), C16:0 (27.5%), and C18:1ω7c and/or C18:1 ω6c (9.2%). The DNA G+C content of the strain was determined to be 67.3 mol%. Based on these data, we propose a novel species within the genus Macromonas, named Macromonas nakdongensis sp. nov., to accommodate the bacterial isolate. Strain BK-30T is designated as the type strain (=KCTC 52161T = JCM 31376T = FBCC-B1). Additionally, we present the isolation and complete genome sequence of a lytic phage infecting strain BK-30T, named BK-30P. This bacteriophage is the first reported to infect Macromonas, leading us to propose the name "Macromonasphage".

Single-Cell Hemoprotein Diet Changes Adipose Tissue Distributions and Re-Shapes Gut Microbiota in High-Fat Diet-Induced Obese Mice.

We have previously observed that feeding with single-cell hemoprotein (heme-SCP) in dogs (1 g/day for 6 days) and broiler chickens (1 ppm for 32 days) increased the proportion of lactic acid bacteria in the gut while reducing their body weights by approximately 1~2%. To define the roles of heme-SCP in modulating body weight and gut microbiota, obese C57BL/6N mice were administered varied heme-SCP concentrations (0, 0.05, and 0.5% heme-SCP in high fat diet) for 28 days. The heme-SCP diet seemed to restrain weight gain till day 14, but the mice gained weight again later, showing no significant differences in weight. However, the heme-SCP-fed mice had stiffer and oilier bodies compared with those of the control mice, which had flabby bodies and dull coats. When mice were dissected at day 10, the obese mice fed with heme-SCP exhibited a reduction in subcutaneous fat with an increase in muscle mass. The effect of heme-SCP on the obesity-associated dyslipidemia tended to be corroborated by the blood parameters (triglyceride, total cholesterol, and C-reactive protein) at day 10, though the correlation was not clear at day 28. Notably, the heme-SCP diet altered gut microbiota, leading to the proliferation of known anti-obesity biomarkers such as Akkermansia, Alistipes, Oscillibacter, Ruminococcus, Roseburia, and Faecalibacterium. This study suggests the potential of heme-SCP as an anti-obesity supplement, which modulates serum biochemistry and gut microbiota in high-fat diet-induced obese mice.

Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent.

This study describes an applied and enhanced real-time vehicle-counting system that is an integral part of intelligent transportation systems. The primary objective of this study was to develop an accurate and reliable real-time system for vehicle counting to mitigate traffic congestion in a designated area. The proposed system can identify and track objects inside the region of interest and count detected vehicles. To enhance the accuracy of the system, we used the You Only Look Once version 5 (YOLOv5) model for vehicle identification owing to its high performance and short computing time. Vehicle tracking and the number of vehicles acquired used the DeepSort algorithm with the Kalman filter and Mahalanobis distance as the main components of the algorithm and the proposed simulated loop technique, respectively. Empirical results were obtained using video images taken from a closed-circuit television (CCTV) camera on Tashkent roads and show that the counting system can produce 98.1% accuracy in 0.2408 s.

Single-cell hemoprotein (heme-SCP) exerts the prebiotic potential to establish a healthy gut microbiota in small pet dogs.

To investigate the effect of the single-cell hemoprotein (heme-SCP) source on animals, a dog-treat (100 g for each dog) harboring 0.2% heme-SCP was manufactured and fed to seven pet dogs (< 10 kg) in a randomized manner (irrespective of owner's feeding style, dogs' health conditions, and staple diets), and the feces before and after the dog-treat diet were analyzed to define the structure of the microbiota. The total bacterial species of the seven dogs showed no difference (564-584), although the bacterial compositions varied significantly. The Firmicutes phylum increased (54.7-73.7%), showing differential species composition before and after heme-SCP intake. Proteobacteria, Bacteroidetes, and Fusobacteria decreased (5.4-3.8%, 32.9-16.8%, and 6.3-3.6%, respectively), which agreed with the previous observation of deliberate feeding. Therefore, it is conceivable that heme-SCP as a prebiotic can shape the gut microbiota regardless of the administration method. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-022-01195-9.

Design and implementation of real-time object detection system based on single-shoot detector and OpenCV.

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient's brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

Does the Summer Season Affect the Amniotic Fluid Volume during Pregnancy?

Amniotic fluid is crucial for the well-being of the fetus. Recent studies suggest that dehydration in a pregnant woman leads to oligohydramnios. We assessed the variation in the amniotic fluid index (AFI) during the summer and non-summer seasons and evaluated neonatal outcomes. We retrospectively reviewed electrical medical records of pregnant women who visited the Konkuk University Medical Center for antenatal care, between July 2005 and July 2019. A total of 19,724 cases from 6438 singleton pregnant women were included after excluding unsuitable cases. All AFI values were classified as 2nd and 3rd trimester values. Additionally, borderline oligohydramnios (AFI, 5-8) and normal AFI (AFI, 8-24) were assessed according to the seasons. The average AFI between the summer and non-summer season was statistically different only in the 3rd trimester; but the results were not clinically significant. In the 3rd trimester, the summer season influenced the increased incidence of borderline oligohydramnios. The borderline oligohydramnios group showed an increased small-for-gestational-age (SGA) rate and NICU admission rate. In the summer season, the incidence of borderline oligohydramnios was seen to increase. This result would be significant for both physicians and pregnant women.

Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network.

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

Genome-scale CRISPR screening identifies cell cycle and protein ubiquitination processes as druggable targets for erlotinib-resistant lung cancer.

Erlotinib is highly effective in lung cancer patients with epidermal growth factor receptor (EGFR) mutations. However, despite initial favorable responses, most patients rapidly develop resistance to erlotinib soon after the initial treatment. This study aims to identify new genes and pathways associated with erlotinib resistance mechanisms in order to develop novel therapeutic strategies. Here, we induced knockout (KO) mutations in erlotinib-resistant human lung cancer cells (NCI-H820) using a genome-scale CRISPR-Cas9 sgRNA library to screen for genes involved in erlotinib susceptibility. The spectrum of sgRNAs incorporated among erlotinib-treated cells was substantially different to that of the untreated cells. Gene set analyses showed a significant depletion of 'cell cycle process' and 'protein ubiquitination pathway' genes among erlotinib-treated cells. Chemical inhibitors targeting genes in these two pathways, such as nutlin-3 and carfilzomib, increased cancer cell death when combined with erlotinib in both in vitro cell line and in vivo patient-derived xenograft experiments. Therefore, we propose that targeting cell cycle processes or protein ubiquitination pathways are promising treatment strategies for overcoming resistance to EGFR inhibitors in lung cancer.

iCSDB: an integrated database of CRISPR screens.

High-throughput screening based on CRISPR-Cas9 libraries has become an attractive and powerful technique to identify target genes for functional studies. However, accessibility of public data is limited due to the lack of user-friendly utilities and up-to-date resources covering experiments from third parties. Here, we describe iCSDB, an integrated database of CRISPR screening experiments using human cell lines. We compiled two major sources of CRISPR-Cas9 screening: the DepMap portal and BioGRID ORCS. DepMap portal itself is an integrated database that includes three large-scale projects of CRISPR screening. We additionally aggregated CRISPR screens from BioGRID ORCS that is a collection of screening results from PubMed articles. Currently, iCSDB contains 1375 genome-wide screens across 976 human cell lines, covering 28 tissues and 70 cancer types. Importantly, the batch effects from different CRISPR libraries were removed and the screening scores were converted into a single metric to estimate the knockout efficiency. Clinical and molecular information were also integrated to help users to select cell lines of interest readily. Furthermore, we have implemented various interactive tools and viewers to facilitate users to choose, examine and compare the screen results both at the gene and guide RNA levels. iCSDB is available at https://www.kobic.re.kr/icsdb/.

EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism.

Recently, studies that analyze emotions based on physiological signals, such as electroencephalogram (EEG), by applying a deep learning algorithm have been actively conducted. However, the study of sequence modeling considering the change of emotional signals over time has not been fully investigated. To consider long-term interaction of emotion, in this study, we propose a long short-term memory network to consider changes in emotion over time and apply an attention mechanism to assign weights to the emotional states appearing at specific moments based on the peak-end rule in psychology. We used 32-channel EEG data from the DEAP database. Two-level (low and high) and three-level (low, middle, and high) classification experiments were performed on the valence and arousal emotion models. The results show accuracies of 90.1% and 87.9% using the two-level classification for the valence and arousal models with four-fold cross validation, respectively. In the case of the three-level classification, these values were obtained as 83.5% and 82.6%, respectively. Additional experiments were conducted using a network combining a convolutional neural network (CNN) submodule with the proposed model. The obtained results showed accuracies of 90.1% and 88.3% in the case of the two-level classification and 86.9% and 84.1% in the case of the three-level classification for the valence and arousal models with four-fold cross validation, respectively. In 10-fold cross validation, there were 91.8% for valence and 91.6% for arousal accuracy, respectively.

NuA3 HAT antagonizes the Rpd3S and Rpd3L HDACs to optimize mRNA and lncRNA expression dynamics.

In yeast, NuA3 histone acetyltransferase (NuA3 HAT) promotes acetylation of histone H3 lysine 14 (H3K14) and transcription of a subset of genes through interaction between the Yng1 plant homeodomain (PHD) finger and H3K4me3. Although NuA3 HAT has multiple chromatin binding modules with distinct specificities, their interdependence and combinatorial actions in chromatin binding and transcription remain unknown. Modified peptide pulldown assays reveal that the Yng1 N-terminal region is important for the integrity of NuA3 HAT by mediating the interaction between core subunits and two methyl-binding proteins, Yng1 and Pdp3. We further uncover that NuA3 HAT contributes to the regulation of mRNA and lncRNA expression dynamics by antagonizing the histone deacetylases (HDACs) Rpd3S and Rpd3L. The Yng1 N-terminal region, the Nto1 PHD finger and Pdp3 are important for optimal induction of mRNA and lncRNA transcription repressed by the Set2-Rpd3S HDAC pathway, whereas the Yng1 PHD finger-H3K4me3 interaction affects transcriptional repression memory regulated by Rpd3L HDAC. These findings suggest that NuA3 HAT uses distinct chromatin readers to compete with two Rpd3-containing HDACs to optimize mRNA and lncRNA expression dynamics.

Lichenicola cladoniae gen. nov., sp. nov., a member of the family Acetobacteraceae isolated from an Antarctic lichen.

Two Gram-stain-negative, facultative anaerobic, chemoheterotrophic, pink-coloured, rod-shaped and non-motile bacterial strains, PAMC 26568 and PAMC 26569T, were isolated from an Antarctic lichen. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strains PAMC 26568 and PAMC 26569T belong to the family Acetobacteraceae and the most closely related species are Gluconacetobacter takamatsuzukensis (96.1 %), Gluconacetobacter tumulisoli (95.9 %) and Gluconacetobacter sacchari (95.7 %). Phylogenomic and genomic relatedness analyses showed that strains PAMC 26568 and PAMC 26569T are clearly distinguished from other genera in the family Acetobacteraceae by average nucleotide identity values (<72.8 %) and the genome-to-genome distance values (<22.5 %). Genomic analysis revealed that strains PAMC 26568 and PAMC 26569T do not contain genes involved in atmospheric nitrogen fixation and utilization of sole carbon compounds such as methane and methanol. Instead, strains PAMC 26568 and PAMC 26569T possess genes to utilize nitrate and nitrite and certain monosaccharides and disaccharides. The major fatty acids (>10 %) are summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c; 40.3-40.4 %), C18 : 1 2OH (22.7-23.7 %) and summed feature 2 (C14 : 0 3OH and/or C16 : 1 iso I; 12.0 % in PAMC 26568). The major respiratory quinone is Q-10. The genomic DNA G+C content of PAMC 26568 and PAMC 26569T is 64.6 %. Their distinct phylogenetic position and some physiological characteristics distinguish strains PAMC 26568 and PAMC 26569T from other genera in the family Acetobacteraceae supporting the proposal of Lichenicola gen. nov., with the type species Lichenicola cladoniae sp. nov. (type strain, PAMC 26569T=KCCM 43315T=JCM 33604T).

Glutathione peroxidase-1 regulates adhesion and metastasis of triple-negative breast cancer cells via FAK signaling.

Triple-negative breast cancer (TNBC) cells, which do not express genes for estrogen receptor (ER), progesterone receptor (PR), and Her2/neu, develop highly aggressive and metastatic tumors resistant to chemo- and hormonal therapies. We found that expression of glutathione peroxidase-1 (Gpx1) is silenced in the non-TNBC cells but significantly maintained in the TNBC cell lines. Such Gpx1 expression plays a vital role in the metastasis of TNBC cells by regulating cell adhesion. Transcriptomic and signaling pathway analyses demonstrate that depletion of Gpx1 essentially impairs cell adhesion/spreading by down-regulating FAK/c-Src activation. Mechanistically, Gpx1 interacts with FAK kinase and prevents the kinase inactivation by H2O2, not lipid hydroperoxide. As a result, depletion of Gpx1 suppresses lung metastasis of TNBC cells in vivo. Overall, our study identifies that Gpx1 is a redox safeguard of FAK kinase and its inhibition may provide an effective way to control the metastasis of deadly malignant TNBC.

Brevibacillus antibioticus sp. nov., with a broad range of antibacterial activity, isolated from soil in the Nakdong River.

A Gram-stain-positive, aerobic, motile, and rod-shaped bacterial strain designated TGS2-1T was isolated from sediment soil in the Nakdong River, Republic of Korea. The optimal growth of strain TGS2-1T was observed at 28°C and pH 7.0 without NaCl supplementation. Strain TGS2-1T revealed antibiosis against various bacteria, including Staphylococcus aureus KCCM 4051, CCARM 3089 (methicillin resistant strains), Enterococcus faecalis KCCM 11814, Escherichia coli KCTC 2443, Candida albicans KACC 7270, and Filobasidium neoformans KCTC 7902. Phylogenetic analyses based on the 16S rRNA gene sequences indicated that strain TGS2-1T belonged to the genus Brevibacillus and shared 93.8-99.7% sequence similarity with Brevibacillus species. Whole-genome sequencing of strain TGS2-1T revealed a genome size of 6.2 Mbp and DNA G + C content of 47.0 mol%. The TGS2-1T genome shared an average nucleotide identity and digital DNA-DNA hybridization of 74.6-93.3% and 18.6-67.1%, respectively, with six related Brevibacillus genomes. The major fatty acid constituents of strain TGS2-1T were anteiso-C15:0 (62.3%) and anteiso-C17:0 (10.8%). Cells of strain TGS2-1T contained diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, seven unidentified aminophospholipids, and five unidentified lipids. The isoprenoid quinone detected in the strain was menaquinone-7 (MK-7). Based on data obtained from this polyphasic taxonomic study, strain TGS2-1T represents a novel species belonging to genus Brevibacillus, for which the name B. antibioticus sp. nov. is proposed. The type strain is TGS2-1T (= KCCM 90326T = NBRC 113840T = FBCC-B2501).

Complete Genome Sequence of Phreatobacter sp. Strain NMCR1094, a Formate-Utilizing Bacterium Isolated from a Freshwater Stream.

Phreatobacter sp. strain NMCR1094 was isolated from a freshwater stream. In this study, we report the complete genome sequence of strain NMCR1094, which contains 4,974,952 bp with 65.8% G+C content and 4,701 predicted coding sequences. In particular, the Phreatobacter sp. NMCR1094 genome contains a formate dehydrogenase region.

Characterization of Distinct CyanoHABs-Related Modules in Microbial Recurrent Association Network.

To elucidate the interspecies connectivity between cyanobacteria and other bacteria (non-cyanobacteria) during cyanobacterial harmful algal blooms (cyanoHABs), samples were collected from the Nakdong River, Korea, from June 2016 to August 2017, and microbial recurrent association network (MRAN) analysis was performed to overcome the limitations of conventional network analysis. Microcystis blooms were tightly linked with Pseudanabaena in summer and were accompanied by significant changes in the non-cyanobacterial community composition (nCCC) compared to non-bloom period. Riverine bacterial communities could be clearly separated into modules that were involved in the formation, maintenance, and decomposition of cyanoHABs. Roseomonas and Herbaspirillum were directly linked with major cyanobacteria and assigned to connector and module hub in cyanoHABs-related modules, respectively. The functional profiles of the cyanoHABs-related modules suggested that nitrate reduction, aerobic ammonia oxidation, fermentation, and hydrocarbon degradation could be increased during the Microcystis bloom periods. In conclusion, MRAN analysis revealed that specific bacteria belonging to cyanoHABs-related module, including connectors and module hubs, appeared to contribute to the development and collapse of cyanoHABs. Therefore, to understand cyanoHABs, a modular microbial perspective may be more helpful than a single bacterial species perspective.